This application pertains to the art of artificial intelligence based systems and more particularly a system in which locomotion control is achieved via adaptations of neural biological concepts.
The invention is particularly applicable to accomplishing motor skill control in an ambulatory insect-like apparatus and will be described with particularly reference thereto. It will be appreciated, however, that the invention has broader applications in modeling performance of high-level human cognitive skills and restricted task domains.
Researchers are becoming increasingly aware that a desirable approach to reacting and controlling within imprecise environments lay with implementation of neural network based systems. These systems provide for an ability to accomplish both supervised and unsupervised learning, and to allow a system to apply learned skills to interact with the imprecise environment.
Fundamental to any progress in actually utilizing artificial intelligence concepts is an ability to model a system in a useful way. A fundamentally sound artificial intelligence ("AI") control system model, in turn, provides a substantial first step to higher-order system modelling.
Although modeling of a simple ambulatory organism presents a first step in a modelling of more complex organisms, such systems provide immediately recognizable real world applications. Autonomous robots are presently under development for use in such diverse applications as military vehicles adapted for rough terrain, as well as unmanned or automated extraterrestrial or underwater exploration.
A comprehensive understanding of such ambulatory insect modeling is found in the following works, authored by the subject inventors and incorporated herein by reference: Periplaneta computatrix: the Artificial Insect Project; Heterogeneous Neural Networks For Adaptive Behavior In Dynamic Environments; A Lesion Study of A Heterogeneous Artificial Neural Network for Hexapod Locomotion; and A Biological Perspective on Autonomous Agent Design.
The present invention contemplates a new and improved system for controlling mobile characteristics of a multi-legged ambulatory unit which provide a readily adaptable autonomous robotic control system. The system is readily realizable by present-day neural networks employing dedicated hardware. Realization is also suitably accomplished by implementation via software in general purpose digital computers. AI languages such as LISP and PROLOG present ideal environments for software realization.